Atypical paroxysmal slow cortical activity in healthy adults: Relationship to age and cognitive performance.

Paroxysmal patterns of slow cortical activity have been detected in EEG recordings from individuals with age-related neuropathology and have been shown to be correlated with cognitive dysfunction and blood-brain barrier disruption in these participants. The prevalence of these events in healthy participants, however, has not been studied. In this work, we inspect MEG recordings from 623 healthy participants from the Cam-CAN dataset for the presence of paroxysmal slow wave events (PSWEs). PSWEs were detected in approximately 20% of healthy participants in the dataset, and participants with PSWEs tended to be older and have lower cognitive performance than those without PSWEs. In addition, event features changed linearly with age and cognitive performance, resulting in longer and slower events in older adults, and more widespread events in those with low cognitive performance. These findings provide the first evidence of PSWEs in a subset of purportedly healthy adults. Going forward, these events may have utility as a diagnostic biomarker for atypical brain activity in older adults.

Using convolutional dictionary learning to detect task-related neuromagnetic transients and ageing trends in a large open-access dataset.

Human neuromagnetic activity is characterised by a complex combination of transient bursts with varying spatial and temporal characteristics. The characteristics of these transient bursts change during task performance and normal ageing in ways that can inform about underlying cortical sources. Many methods have been proposed to detect transient bursts, with the most successful ones being those that employ multi-channel, data-driven approaches to minimize bias in the detection procedure. There has been little research, however, into the application of these data-driven methods to large datasets for group-level analyses. In the current work, we apply a data-driven convolutional dictionary learning (CDL) approach to detect neuromagnetic transient bursts in a large group of healthy participants from the Cam-CAN dataset. CDL was used to extract repeating spatiotemporal motifs in 538 participants between the ages of 18-88 during a sensorimotor task. Motifs were then clustered across participants based on similarity, and relevant task-related clusters were analysed for age-related trends in their spatiotemporal characteristics. Seven task-related motifs resembling known transient burst types were identified through this analysis, including beta, mu, and alpha type bursts. All burst types showed positive trends in their activation levels with age that could be explained by increasing burst rate with age. This work validated the data-driven CDL approach for transient burst detection on a large dataset and identified robust information about the complex characteristics of human brain signals and how they change with age.

Age-related trends in the cortical sources of transient beta bursts during a sensorimotor task and rest.

Interpreting neurophysiology recordings as a series of transient bursts with varying temporal and spectral characteristics provides meaningful insight into mechanisms underlying neural networks. Previous research has revealed age-related changes in the time-frequency dynamics of sensorimotor beta bursts, but to date, there has been little focus on the spatial localization of these beta bursts or how the localization patterns change with normal healthy ageing. The objective of the current study is to implement existing source localization algorithms for use in the detection of the cortical sources of transient beta bursts, and to uncover age-related trends in the resulting source localization patterns. Two well-established source localization algorithms (minimum-norm estimation and beamformer) were applied to localize beta bursts detected over the sensorimotor cortices in a cohort of 561 healthy participants between the ages of 18 and 88 (CamCAN open access dataset). Age-related trends were then investigated by applying regression analysis between participant age and average source power within several cortical regions of interest. This analysis revealed that beta bursts localized primarily to the sensorimotor cortex ipsilateral to the side of the sensor used for their detection. Region of interest analysis revealed that there were age-related changes in the beta burst localization pattern, with most substantial changes evidenced in frontal brain regions. In addition, regression analysis revealed a tendency of age-related trends to peak around 60 years of age suggesting that 60 is a potential critical age in this population. These results show for the first time that source localization techniques can be implemented for the identification of the sources of transient beta bursts. The exploration of these sources provides us with insight into the anatomical generators of transient beta activity and how they change across the lifespan.

Representational Rényi Heterogeneity.

A discrete system's heterogeneity is measured by the Rényi heterogeneity family of indices (also known as Hill numbers or Hannah-Kay indices), whose units are the numbers equivalent. Unfortunately, numbers equivalent heterogeneity measures for non-categorical data require a priori (A) categorical partitioning and (B) pairwise distance measurement on the observable data space, thereby precluding application to problems with ill-defined categories or where semantically relevant features must be learned as abstractions from some data. We thus introduce representational Rényi heterogeneity (RRH), which transforms an observable domain onto a latent space upon which the Rényi heterogeneity is both tractable and semantically relevant. This method requires neither a priori binning nor definition of a distance function on the observable space. We show that RRH can generalize existing biodiversity and economic equality indices. Compared with existing indices on a beta-mixture distribution, we show that RRH responds more appropriately to changes in mixture component separation and weighting. Finally, we demonstrate the measurement of RRH in a set of natural images, with respect to abstract representations learned by a deep neural network. The RRH approach will further enable heterogeneity measurement in disciplines whose data do not easily conform to the assumptions of existing indices.

Age-related trends in neuromagnetic transient beta burst characteristics during a sensorimotor task and rest in the Cam-CAN open-access dataset.

Non-invasive neurophysiological recordings, such as those measured by magnetoencelography (MEG), provide insight into the behaviour of neural networks and how these networks change with factors such as task performance, disease state, and age. Recently, there has been a trend in describing neurophysiological recordings as a series of transient bursts of neural activity rather than averaged sustained oscillations as burst characteristics may be more directly correlated with the neurological generators of brain activity. In this work, we investigate how beta burst characteristics change with age in a large open access dataset. The objectives are (1) to detect and characterize transient beta bursts over the ipsilateral and contralateral primary sensorimotor cortices during a unilateral motor task performance and during wakeful resting, and (2) to identify age-related changes in beta burst characteristics, in the context of earlier reports of age-related changes in beta suppression and the post-movement beta rebound. MEG data, acquired at the Cambridge Centre for Ageing and Neuroscience, of roughly 600 participants with a nearly uniform distribution of ages between 18 and 88 years old was used for analysis. We found that burst rate is the predominant factor related to age-related changes in the amplitude of the induced beta rhythm responses associated with a button press task. Furthermore, we present a cross-validation of burst parameters detected at the sensor- (peak sensor and sensor ROI) and source-level (beamformer spatial filter). This work is as an important step in characterizing transient bursts in neuromagnetic signals in the temporal domain, towards a better understanding of the healthy aging human brain.

Variability and bias between magnetoencephalography systems in localization of the primary visual cortex.

OBJECTIVES: When using MEG for pre-surgical mapping it is critically important that reliable estimates of functional locations, such as the primary visual cortex (V1) can be provided. Several different models of MEG systems exist, each with varying software and hardware configurations, and it is not currently known how the system type contributes to variability in V1 localization. PATIENTS AND METHODS: In this study, participants underwent MEG sessions using two different systems (Vector View and CTF) during which they were presented with a repeating grating stimulus to the lower-left visual quadrant to generate a visual evoked field (VEF). The location, amplitude and latency of the VEF source was compared between systems for each participant. RESULTS: No significant differences were found in latency and amplitude between systems, however, a significant bias in the latero-medial position of the localization was present. The median inter-system Euclidian distance between V1 localization across participants was 10.5 mm. CONCLUSIONS: Overall, our results indicate that mapping of V1 can be reliably reproduced within approximately one centimetre by different MEG systems. SIGNIFICANCE: This result provides knowledge of the useful limits on the reliability of localization which can be taken into consideration in clinical practice.

Efficacy of low-cost wireless neurofeedback to modulate brain activity during motor imagery.

OBJECTIVES: Motor imagery can be used as an adjunct to traditional stroke rehabilitation therapies for individuals who have hand and arm impairment resulting from their stroke. The provision of neurofeedback during motor imagery allows individuals to receive real time information regarding their motor imagery-related brain activity. However, the equipment required to administer this feedback is expensive and largely inaccessible to many of the individuals who could benefit from it. Available EEG-based technology provides an accessible, low-cost, wireless alternative to traditional neurofeedback methods, with the tradeoff of lower gain and channel count resulting in reduced signal quality. This study investigated the efficacy of this wireless technology for the provision of motor imagery-related neurofeedback. APPROACH: Twenty-eight healthy individuals participated in a 2-group, double-blinded study which involved imagining performing a unimanual button pressing task while receiving neurofeedback that is either a direct transform of their motor imagery-related brain activity (i.e., real) or is related to someone else's brain activity (i.e., sham). The change in amplitude of 15-30 Hz (beta) rhythmic brain activity elicited during the task blocks was calculated and analyzed across sessions and groups. MAIN RESULTS: We found that individuals who received real neurofeedback showed a statistically significant positive trajectory in modulating the amplitude of the beta rhythm across sessions, while those who received sham feedback showed a negative trajectory. Our results did not indicate a trend of increased lateralization across sessions, as has been shown in previous studies. SIGNIFICANCE: Our main findings replicated previous results with research-grade equipment indicating that there is potential for introducing this wireless technology for the provision of neurofeedback. Given the marginal longitudinal effect of neurofeedback in our study, further study is required to address the limitations associated with this technology before our protocol can be implemented in a clinical setting.

Probing the temporal dynamics of movement inhibition in motor imagery.

Beyond the lack of overt movement in motor imagery (MI), MI is thought to be functionally equivalent to motor execution (ME). Two theories appear viable to explain the neural mechanism underlying the inhibition of movement in MI, with one suggesting the inhibition of movement in MI occurs early in the planning process, and the other suggesting it occurs after the planning for movement is compete. Here we sought to generate evidence related to the timing of movement inhibition in MI. Participants performed a motor task via MI and ME that had distinct preparation and performance phases, with brain activity obtained throughout. Analysis of sensor-level data was performed to isolate event related desynchrony (ERD) in the mu and beta frequency bands in both a sensorimotor and left parietal region of interest (ROI). The magnitude of ERD in the sensorimotor ROI was significantly greater in ME than MI during both the preparatory and performance phases. The reduced ERD in the mu and beta frequency bands in the sensorimotor ROI during the preparatory phase for MI, compared to ME, suggests that movement planning is inhibited (or at least reduced) in MI, contributing to the lack of movement. While past work has shown that the networks of functional brain activity underlying MI and ME are heavily overlapping, differences in the temporal dynamics of this activity suggest that MI and ME are not equivalent processes.

Evidence for age-related changes in sensorimotor neuromagnetic responses during cued button pressing in a large open-access dataset.

Mu, beta, and gamma rhythms increase and decrease in amplitude during movement. This event-related synchronization (ERS) and desynchronization (ERD) can be readily recorded non-invasively using magneto- and electro-encephalography (M/EEG). In addition, event-related potentials and fields (i.e., evoked responses) can be elucidated during movement. There is some evidence that the frequency, amplitude and latency of the movement-related ERS/ERD changes with ageing, however the evidence surrounding this topic comes mainly from studies in sample sizes on the order of tens of participants. The objective of this study was to examine a large open-access MEG dataset for age-related changes in movement-related ERS/ERD and evoked responses. MEG data acquired at the Cambridge Centre for Ageing and Neuroscience during cued button pressing was used from 567 participants between the ages of 18 and 88 years. The characteristics movement-related ERD/ERS and evoked responses were calculated for each individual participant. Based on linear regression analysis, significant relationships were found between participant age and some response characteristics, although the predictive value of these relationships was low. Specifically, we conclude that peak beta rebound frequency and amplitude decreased with age, peak beta suppression amplitude increased with age, movement-related gamma burst amplitude decreased with age, and peak motor-evoked response amplitude increased with age. Given our current understanding of the underlying mechanisms of these responses, our findings suggest the existence of age-related changes in the neurophysiology of thalamocortical loops and local circuitry in the primary somatosensory and motor cortices.

Variability and bias between magnetoencephalography systems in non-invasive localization of the primary somatosensory cortex.

OBJECTIVES: Magnetoencephalography (MEG) provides functional neuroimaging data for pre-surgical planning in patients with epilepsy or brain tumour. For mapping the primary somatosensory cortex (S1), MEG data are acquired while a patient undergoes median nerve stimulation (MNS) to localize components of the somatosensory evoked field (SEF). In clinical settings, only one MEG imaging session is usually possible due to limited resources. As such, it is important to have an a priori estimate of the expected variability in localization. Variability in S1 localization between mapping sessions using the same MEG system has been previously measured as 8 mm. There are different types of MEG systems available with varied hardware and software, and it is not known how using a different MEG system will impact on S1 localization. PATIENTS AND METHODS: In our study, healthy participants underwent the MNS procedure with two different MEG systems (Vector View and CTF). We compared the location, amplitude and latency of SEF components between data from each system to quantify variability and bias between MEG systems. RESULTS: We found 8-11 mm variability in S1 localization between the two MEG systems, and no evidence for a systematic bias in location, amplitude or latency between the two systems. CONCLUSION: These findings suggest that S1 localization is not biased by the type of MEG system used, and that differences between the two systems are not a major contributor to variability in localization.

Combined Action Observation and Motor Imagery Neurofeedback for Modulation of Brain Activity.

Motor imagery (MI) and action observation have proven to be efficacious adjuncts to traditional physiotherapy for enhancing motor recovery following stroke. Recently, researchers have used a combined approach called imagined imitation (II), where an individual watches a motor task being performed, while simultaneously imagining they are performing the movement. While neurofeedback (NFB) has been used extensively with MI to improve patients' ability to modulate sensorimotor activity and enhance motor recovery, the effectiveness of using NFB with II to modulate brain activity is unknown. This project tested the ability of participants to modulate sensorimotor activity during electroencephalography-based II-NFB of a complex, multi-part unilateral handshake, and whether this ability transferred to a subsequent bout of MI. Moreover, given the goal of translating findings from NFB research into practical applications, such as rehabilitation, the II-NFB system was designed with several user interface and user experience features, in an attempt to both drive user engagement and match the level of challenge to the abilities of the subjects. In particular, at easy difficulty levels the II-NFB system incentivized contralateral sensorimotor up-regulation (via event related desynchronization of the mu rhythm), while at higher difficulty levels the II-NFB system incentivized sensorimotor lateralization (i.e., both contralateral up-regulation and ipsilateral down-regulation). Thirty-two subjects, receiving real or sham NFB attended four sessions where they engaged in II-NFB training and subsequent MI. Results showed the NFB group demonstrated more bilateral sensorimotor activity during sessions 2-4 during II-NFB and subsequent MI, indicating mixed success for the implementation of this particular II-NFB system. Here we discuss our findings in the context of the design features included in the II-NFB system, highlighting limitations that should be considered in future designs.

Asymmetric Weighting to Optimize Regional Sensitivity in Combined fMRI-MEG Maps.

Functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) are neuroimaging techniques that measure inherently different physiological processes, resulting in complementary estimates of brain activity in different regions. Combining the maps generated by each technique could thus provide a richer understanding of brain activation. However, present approaches to integration rely on a priori assumptions, such as expected patterns of brain activation in a task, or use fMRI to bias localization of MEG sources, diminishing fMRI-invisible sources. We aimed to optimize sensitivity to neural activity by developing a novel method of integrating data from the two imaging techniques. We present a data-driven method of integration that weights fMRI and MEG imaging data by estimates of data quality for each technique and region. This method was applied to a verbal object recognition task. As predicted, the two imaging techniques demonstrated sensitivity to activation in different regions. Activity was seen using fMRI, but not MEG, throughout the medial temporal lobes. Conversely, activation was seen using MEG, but not fMRI, in more lateral and anterior temporal lobe regions. Both imaging techniques were sensitive to activation in the inferior frontal gyrus. Importantly, integration maps retained activation from individual activation maps, and showed an increase in the extent of activation, owing to greater sensitivity of the integration map than either fMRI or MEG alone.

Reliability for non-invasive somatosensory cortex localization: Implications for pre-surgical mapping.

OBJECTIVES: In patients with epilepsy or space occupying tumors in cortical regions, surgical resection is often considered as the primary treatment. Pre-surgical neuroimaging can provide a detailed map of pathological and functional cortex, leading to safer surgery. Mapping can be achieved non-invasively using magnetoencephalography (MEG), and is concordant with invasive findings. However, the reliability of MEG mapping between sessions is not well established. The inter-session reliability is an important property in pre-surgical mapping to establish resection margins, but repeated scans are impracticable. The present study sought to quantify the intersession reliability of MEG localization of somatosensory cortex (S1). PATIENTS AND METHODS: Eighteen healthy individuals underwent MEG sessions on 3 consecutive days. Five participants were excluded due to technical issues during one of the three days. Each session included clinical-style S1 localization using electrical stimuli to each median nerve at sub-motor thresholds. The 35 ms peak of the somatosensory evoked field was used for localizing S1 in each session using a single equivalent current dipole model. Intersession reliability was quantified using two methods. Average Euclidean Distance (AED) quantified the difference in localization between each session and the inter-session mean localization. Session Euclidean Distance (SED) quantified the difference in localization between each pair of sessions. RESULTS AND DISCUSSION: Results showed the AED was 4.8 ± 1.9 mm, whereas the SED was 8.3 ± 3.4mm. While the AED values obtained parallel those reported previously in smaller samples, the SED values were substantially larger. CONCLUSION: Clinicians should consider up to an 8mm confidence interval around the estimated location of S1 based on MEG pre-surgical mapping.

Faster and improved 3-D head digitization in MEG using Kinect.

Accuracy in localizing the brain areas that generate neuromagnetic activity in magnetoencephalography (MEG) is dependent on properly co-registering MEG data to the participant's structural magnetic resonance image (MRI). Effective MEG-MRI co-registration is, in turn, dependent on how accurately we can digitize anatomical landmarks on the surface of the head. In this study, we compared the performance of three devices-Polhemus electromagnetic system, NextEngine laser scanner and Microsoft Kinect for Windows-for source localization accuracy and MEG-MRI co-registration. A calibrated phantom was used for verifying the source localization accuracy. The Kinect improved source localization accuracy over the Polhemus and the laser scanner by 2.23 mm (137%) and 0.81 mm (50%), respectively. MEG-MRI co-registration accuracy was verified on data from five healthy human participants, who received the digitization process using all three devices. The Kinect device captured approximately 2000 times more surface points than the Polhemus in one third of the time (1 min compared to 3 min) and thrice as many points as the NextEngine laser scanner. Following automated surface matching, the calculated mean MEG-MRI co-registration error for the Kinect was improved by 2.85 mm with respect to the Polhemus device, and equivalent to the laser scanner. Importantly, the Kinect device automatically aligns 20-30 images per second in real-time, reducing the limitations on participant head movement during digitization that are implicit in the NextEngine laser scan (~1 min). We conclude that the Kinect scanner is an effective device for head digitization in MEG, providing the necessary accuracy in source localization and MEG-MRI co-registration, while reducing digitization time.

Motor imagery-based brain activity parallels that of motor execution: evidence from magnetic source imaging of cortical oscillations.

Motor imagery (MI) is a form of practice in which an individual mentally performs a motor task. Previous research suggests that skill acquisition via MI is facilitated by repetitive activation of brain regions in the sensorimotor network similar to that of motor execution, however this evidence is conflicting. Further, many studies do not control for overt muscle activity and thus the activation patterns reported for MI may be driven in part by actual movement. The purpose of the current research is to further establish MI as a secondary modality of skill acquisition by providing electrophysiological evidence of an overlap between brain areas recruited for motor execution and imagery. Non-disabled participants (N=18; 24.7±3.8 years) performed both execution and imagery of a unilateral sequence button-press task. Magnetoencephalography (MEG) was utilized to capture neural activity, while electromyography used to rigorously monitor muscle activity. Event-related synchronization/desynchronization (ERS/ERD) analysis was conducted in the beta frequency band (15-30 Hz). Whole head dual-state beamformer analysis was applied to MEG data and 3D t-tests were conducted after Talairach normalization. Source-level analysis showed that MI has similar patterns of spatial activity as ME, including activation of contralateral primary motor and somatosensory cortices. However, this activation is significantly less intense during MI (p<0.05). As well, activation during ME was more lateralized (i.e., within the contralateral hemisphere). These results confirm that ME and MI have similar spatial activation patterns. Thus, the current research provides direct electrophysiological evidence to further establish MI as a secondary form of skill acquisition.

Laterality of brain activity during motor imagery is modulated by the provision of source level neurofeedback.

Motor imagery (MI) may be effective as an adjunct to physical practice for motor skill acquisition. For example, MI is emerging as an effective treatment in stroke neurorehabilitation. As in physical practice, the repetitive activation of neural pathways during MI can drive short- and long-term brain changes that underlie functional recovery. However, the lack of feedback about MI performance may be a factor limiting its effectiveness. The provision of feedback about MI-related brain activity may overcome this limitation by providing the opportunity for individuals to monitor their own performance of this endogenous process. We completed a controlled study to isolate neurofeedback as the factor driving changes in MI-related brain activity across repeated sessions. Eighteen healthy participants took part in 3 sessions comprised of both actual and imagined performance of a button press task. During MI, participants in the neurofeedback group received source level feedback based on activity from the left and right sensorimotor cortex obtained using magnetoencephalography. Participants in the control group received no neurofeedback. MI-related brain activity increased in the sensorimotor cortex contralateral to the imagined movement across sessions in the neurofeedback group, but not in controls. Task performance improved across sessions but did not differ between groups. Our results indicate that the provision of neurofeedback during MI allows healthy individuals to modulate regional brain activity. This finding has the potential to improve the effectiveness of MI as a tool in neurorehabilitation.

Head movement compensation in real-time magnetoencephalographic recordings.

Neurofeedback- and brain-computer interface (BCI)-based interventions can be implemented using real-time analysis of magnetoencephalographic (MEG) recordings. Head movement during MEG recordings, however, can lead to inaccurate estimates of brain activity, reducing the efficacy of the intervention. Most real-time applications in MEG have utilized analyses that do not correct for head movement. Effective means of correcting for head movement are needed to optimize the use of MEG in such applications. Here we provide preliminary validation of a novel analysis technique, real-time source estimation (rtSE), that measures head movement and generates corrected current source time course estimates in real-time. rtSE was applied while recording a calibrated phantom to determine phantom position localization accuracy and source amplitude estimation accuracy under stationary and moving conditions. Results were compared to off-line analysis methods to assess validity of the rtSE technique. The rtSE method allowed for accurate estimation of current source activity at the source-level in real-time, and accounted for movement of the source due to changes in phantom position. The rtSE technique requires modifications and specialized analysis of the following MEG work flow steps.•Data acquisition•Head position estimation•Source localization•Real-time source estimation This work explains the technical details and validates each of these steps.

Spatial MEG laterality maps for language: clinical applications in epilepsy.

Functional imaging is increasingly being used to provide a noninvasive alternative to intracarotid sodium amobarbitol testing (i.e., the Wada test). Although magnetoencephalography (MEG) has shown significant potential in this regard, the resultant output is often reduced to a simplified estimate of laterality. Such estimates belie the richness of functional imaging data and consequently limit the potential value. We present a novel approach that utilizes MEG data to compute "complex laterality vectors" and consequently "laterality maps" for a given function. Language function was examined in healthy controls and in people with epilepsy. When compared with traditional laterality index (LI) approaches, the resultant maps provided critical information about the magnitude and spatial characteristics of lateralized function. Specifically, it was possible to more clearly define low LI scores resulting from strong bilateral activation, high LI scores resulting from weak unilateral activation, and most importantly, the spatial distribution of lateralized activation. We argue that the laterality concept is better presented with the inherent spatial sensitivity of activation maps, rather than being collapsed into a one-dimensional index.

State-related changes in MEG functional connectivity reveal the task-positive sensorimotor network.

Functional connectivity measures applied to magnetoencephalography (MEG) data have the capacity to elucidate neuronal networks. However, the task-related modulation of these measures is essential to identifying the functional relevance of the identified network. In this study, we provide evidence for the efficacy of measuring "state-related" (i.e., task vs. rest) changes in MEG functional connectivity for revealing a sensorimotor network. We investigate changes in functional connectivity, measured as cortico-cortical coherence (CCC), between rest blocks and the performance of a visually directed motor task in a healthy cohort. Task-positive changes in CCC were interpreted in the context of any concomitant modulations in spectral power. Task-related increases in whole-head CCC relative to the resting state were identified between areas established as part of the sensorimotor network as well as frontal eye fields and prefrontal cortices, predominantly in the beta and gamma frequency bands. This study provides evidence for the use of MEG to identify task-specific functionally connected sensorimotor networks in a non-invasive, patient friendly manner.

Improved localization accuracy in magnetic source imaging using a 3-D laser scanner.

Brain source localization accuracy in magnetoencephalography (MEG) requires accuracy in both digitizing anatomical landmarks and coregistering to anatomical magnetic resonance images (MRI). We compared the source localization accuracy and MEG-MRI coregistration accuracy of two head digitization systems-a laser scanner and the current standard electromagnetic digitization system (Polhemus)-using a calibrated phantom and human data. When compared using the calibrated phantom, surface and source localization accuracy for data acquired with the laser scanner improved over the Polhemus by 141% and 132%, respectively. Laser scan digitization reduced MEG source localization error by 1.38 mm on average. In human participants, a laser scan of the face generated a 1000-fold more points per unit time than the Polhemus head digitization. An automated surface-matching algorithm improved the accuracy of MEG-MRI coregistration over the equivalent manual procedure. Simulations showed that the laser scan coverage could be reduced to an area around the eyes only while maintaining coregistration accuracy, suggesting that acquisition time can be substantially reduced. Our results show that the laser scanner can both reduce setup time and improve localization accuracy, in comparison to the Polhemus digitization system.